Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks

Rule, Adam; Birmingham, Amanda; Zuñiga, Cristal; Altıntaş, Ilkay; Huang, Shih-Cheng; Knight, Rob; Moshiri, Niema; Nguyen, Manh Duc; Rosenthal, Sara Brin; Pérez, Fernando; Rose, Peter W.

doi:10.1371/journal.pcbi.1007007

Cited by 122 publications

(132 citation statements)

References 10 publications

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“…As such, Jupyter notebook is a popular platform in which these workflows are coded, owing to its intuitive blend of code and documentation. Particularly, the ten simple rules for best practice in documenting cheminformatics research using the Jupyter notebook is a useful and timely guideline [265]. These documentations can also be found on GitHub, where a number of researchers share the code to their project's workflow.…”

Section: Workflows For Computational Drug Discoverymentioning

confidence: 99%

Towards reproducible computational drug discovery

et al. 2020

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The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilization for data collection, pre-processing, analysis and inference. This article provides an in-depth coverage on the reproducibility of computational drug discovery. This review explores the following topics: (1) the current state-of-the-art on reproducible research, (2) research documentation (e.g. electronic laboratory notebook, Jupyter notebook, etc.), (3) science of reproducible research (i.e. comparison and contrast with related concepts as replicability, reusability and reliability), (4) model development in computational drug discovery, (5) computational issues on model development and deployment, (6) use case scenarios for streamlining the computational drug discovery protocol. In computational disciplines, it has become common practice to share data and programming codes used for numerical calculations as to not only facilitate reproducibility, but also to foster collaborations (i.e. to drive the project further by introducing new ideas, growing the data, augmenting the code, etc.). It is therefore inevitable that the field of computational drug design would adopt an open approach towards the collection, curation and sharing of data/code.

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Section: Workflows For Computational Drug Discoverymentioning

confidence: 99%

Towards reproducible computational drug discovery

et al. 2020

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“…The example shows a minimalistic analysis of the "Considerations of Future Consequences (CFC) Scale". The analysis demonstrates a successful implementation of our workflow including downloads of external data, comparison of their integrity using a checksum, and the running of a confirmatory factor analysis on the first few items using lavaan (Rosseel, 2012…”

Section: Reproducing An Analysismentioning

confidence: 99%

“…Here, we combine four software tools, whose interplay can guarantee full computational reproducibility of data analyses and their reporting. There are various ideas on how to enhance reproducibility (Piccolo & Frampton, 2016), four of which we believe to be particularly important: literate programming: (Rule et al, 2019), version control (Barba, 2016), dependency management (Askren et al, 2016), and containerization (Clyburne-Sherin, Fei, & Green, 2018). We argue that only a workflow using all four concepts in unison can guarantee confidence in reproducing a scientific report.…”

Section: Introductionmentioning

confidence: 99%

A Reproducible Data Analysis Workflow with R Markdown, Git, Make, and Docker

Peikert¹,

Brandmaier²

2019

Preprint

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In this tutorial, we describe a workflow to ensure long-term reproducibility of R-based data analyses. The workflow leverages established tools and practices from software engineering. It combines the benefits of various open-source software tools including R Markdown, Git, Make, and Docker, whose interplay ensures seamless integration of version management, dynamic report generation conforming to various journal styles and full cross-platform and long-term computational reproducibility. The workflow ensures meeting the primary goals that 1) the reporting of statistical results is consistent with the actual statistical results (dynamic report generation), the analysis exactly reproduces at a later time even if the computing platform or software is changed (computational reproducibility), and 3) changes at any time (during development and post-publication) are tracked, tagged, and documented while earlier versions of both data and code remain accessible. While the research community increasingly recognizes dynamic document generation and version management as tools to ensure reproducibility, we demonstrate with practical examples that these alone are not sufficient to ensure long-term computational reproducibility. Leveraging containerization, dependence management, version management, and literate programming, the workflow increases scientific productivity by facilitating later reproducibility and reuse of code and data.

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“…As an example, the above-mentioned UMAP dimensionality reduction was readily imported through the corresponding library (McInnes et al, 2018). Our integration of Jupyter Notebooks, an open source tool that allows mixing of text, graphics, code and data in a single document (Lamb et al, 2006;Mendez et al, 2019;Perkel, 2018;Rule et al, 2019) enables standard or bespoke analysis pipelines, including addition of existing or user-implemented functionality from the Python or R ecosystems.…”

Section: Figure 1 the Clinical Knowledge Graph Architecture A) Ckg mentioning

confidence: 99%

Clinical Knowledge Graph Integrates Proteomics Data into Clinical Decision-Making

Santos

Colaço

Nielsen

et al. 2020

Preprint

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The promise of precision medicine is to deliver personalized treatment based on the unique physiology of each patient. This concept was fueled by the genomic revolution, but it is now evident that integrating other types of omics data, like proteomics, into the clinical decisionmaking process will be essential to accomplish precision medicine goals. However, quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across myriad biomedical databases and publications makes this exceptionally difficult. To address this, we developed the Clinical Knowledge Graph (CKG), an open source platform currently comprised of more than 16 million nodes and 220 million relationships to represent relevant experimental data, public databases and the literature. The CKG also incorporates the latest statistical and machine learning algorithms, drastically accelerating analysis and interpretation of typical proteomics workflows. We use several biomarker studies to illustrate how the CKG may support, enrich and accelerate clinical decision-making. Graphical Abstract

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Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks

Cited by 122 publications

References 10 publications

Towards reproducible computational drug discovery

Towards reproducible computational drug discovery

A Reproducible Data Analysis Workflow with R Markdown, Git, Make, and Docker

Clinical Knowledge Graph Integrates Proteomics Data into Clinical Decision-Making

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